arXiv subjects

Yuki Koizumi

Publications and source records attributed to Yuki Koizumi.

8 recordsLinked to original sources

arXiv2026

Lacan: Making Accountability in Anonymous Networks Real

Anonymity and accountability are essential properties for our everyday activity on the Internet. However, they appear contradictory, and their reconciliation remains far from reality. Existing approaches fall short in this regard, as they either rely on an on-path trustee, per-packet authorization, per-packet public-key cryptography, or per-session intervention by a central authority. We propose Lacan, a protocol that reconciles anonymity and accountability within a realistic design. In Lacan, a sender enjoys anonymity provided by on-path relays, as long as she complies with a contract established with the receiver. Upon a contract violation, the verifier, an off-path trustee on behalf of the receiver, links the malicious message to the sender's identity indirectly via the packet, path, and session, thereby reducing public-key operations from per-packet to per-session. This linkage remains robust even against malicious relays and receivers, grounded in our novel chain of successor proofs for accountable path reconstruction, together with traceable signatures, path validation, and key-committing encryption. We analyze the anonymity and accountability, implement the protocol, and evaluate the performance.

cs.NI↗

arXiv2026

Provably Efficient Learning of Fermionic Correlations under Particle-Number Symmetry

Predicting local fermionic correlations is a central task in quantum many-body physics, as these correlations encode many physically relevant local observables. The ubiquitous particle-number symmetry imposes strong structural constraints on quantum states, suggesting that local correlations should be learned with fewer samples than by symmetry-agnostic approaches. However, it has remained unclear whether such a provable advantage exists in collective learning of local correlations. Here, we develop a framework of number-conserving fermionic-shadow tomography based on random orbital rotations. We prove that, for every given order $k$, we can simultaneously estimate {\it all} $k$-body fermionic correlations of an $N$-mode $η$-particle state with a given variance $\varepsilon^2$ using only $O_k(η^k/\varepsilon^2)$ samples, which are independent of the system size $N$. We further establish a matching information-theoretic lower bound $Ω_k(η^k/\varepsilon^2)$ for any adaptive protocol based on single-copy measurements, showing that the $(η^k,\varepsilon)$-dependence is optimal up to constants depending only on $k$. Furthermore, our numerical calculation shows that the proposal reduces the query count by roughly an order of magnitude compared with state-of-the-art methods for one-body correlation estimation in a system of $N=100$, $η=20$ at $\varepsilon=10^{-2}$. This work establishes a provably efficient advantage of particle-number symmetry for fermionic observables estimation.

quant-ph↗

arXiv2026

One-to-One Correspondence between Deterministic Port-Based Teleportation and Unitary Estimation

Port-based teleportation is a variant of quantum teleportation, where the receiver can choose one of the ports in his part of the entangled state shared with the sender, but cannot apply other recovery operations. We show that the optimal fidelity of deterministic port-based teleportation (dPBT) using $N=n+1$ ports to teleport a $d$-dimensional state is equivalent to the optimal fidelity of $d$-dimensional unitary estimation using $n$ calls of the input unitary operation. From any given dPBT, we can explicitly construct the corresponding unitary estimation protocol achieving the same optimal fidelity, and vice versa. Using the obtained one-to-one correspondence between dPBT and unitary estimation, we derive the asymptotic optimal fidelity of port-based teleportation given by $1-O(d^4)N^{-2}\leq F \leq 1-Ω(d^4)N^{-2}$, which improves the previously known result given by $1-O(d^5)N^{-2} \leq F \leq 1-Ω(d^2) N^{-2}$. We also show that the optimal fidelity of unitary estimation for the case $n\leq d-1$ is $F = {n+1 \over d^2}$, and this fidelity is equal to the optimal fidelity of unitary inversion with $n\leq d-1$ calls of the input unitary operation even if we allow indefinite causal order among the calls.

quant-ph↗

arXiv2026

immUNITY: Detecting and Mitigating Low Volume & Slow Attacks with Programmable Switches and SmartNICs

Our analysis of recent Internet traces shows that up to 71% of flows contain suspicious behaviors indicative of low-volume network attacks such as port scans. However, distinguishing anomalous traffic in real time is challenging as each attack flow may comprise only a few packets. We extend prior work that tracks heavy hitter flows to also detect low-volume and slow attacks by combining the capabilities of both switches and SmartNICs. We flip the usual design approach by proposing an efficient filter data structure used to quickly route traffic marked as benign towards destination end-systems. We make careful use of limited programmable switch memory and pipeline stages, and complement them with SmartNIC resources to analyze the remaining traffic that may be anomalous. Using machine learning classifiers and intrusion detection rules deployed on the SmartNIC, we identify malicious source IPs, which then undergo more detailed forensics for attack mitigation. Finally, we develop a dataplane based protocol to rapidly coordinate data structure updates between these devices. We implement immUNITY in a testbed with Tofino v1 switch and Bluefield 3 SmartNIC, demonstrating its high accuracy, while minimizing traffic that's analyzed outside the switch.

cs.NI↗

arXiv2025

Per-element Secure Aggregation against Data Reconstruction Attacks in Federated Learning

Federated learning (FL) enables collaborative model training without sharing raw data, but individual model updates may still leak sensitive information. Secure aggregation (SecAgg) mitigates this risk by allowing the server to access only the sum of client updates, thereby concealing individual contributions. However, a significant vulnerability has recently attracted increasing attention: when model updates are sparse vectors, a non-zero value contributed by a single client at a given index can be directly revealed in the aggregate, enabling precise data reconstruction attacks. In this paper, we propose a novel enhancement to SecAgg that reveals aggregated values only at indices with at least $t$ non-zero contributions. Our mechanism introduces a per-element masking strategy to prevent the exposure of under-contributed elements, while maintaining modularity and compatibility with many existing SecAgg implementations by relying solely on cryptographic primitives already employed in a typical setup. We integrate this mechanism into Flamingo, a low-round SecAgg protocol, to provide a robust defense against such attacks. Our analysis and experimental results indicate that the additional computational and communication overhead introduced by our mechanism remains within an acceptable range, supporting the practicality of our approach.

cs.CR↗

arXiv2025

Faster Quantum Algorithm for Multiple Observables Estimation in Fermionic Problems

Achieving quantum advantage in efficiently estimating collective properties of quantum many-body systems remains a fundamental goal in quantum computing. While the quantum gradient estimation (QGE) algorithm has been shown to achieve doubly quantum enhancement in the precision and the number of observables, it remains unclear whether one benefits in practical applications. In this work, we present a generalized framework of adaptive QGE algorithm, and further propose two variants which enable us to estimate the collective properties of fermionic systems using the smallest cost among existing quantum algorithms. The first method utilizes the symmetry inherent in the target state, and the second method enables estimation in a single-shot manner using the parallel scheme. We show that our proposal offers a quadratic speedup compared with prior QGE algorithms in the task of fermionic partial tomography for systems with limited particle numbers. Furthermore, we provide the numerical demonstration that, for a problem of estimating fermionic 2-RDMs, our proposals improve the number of queries to the target state preparation oracle by a factor of 100 for the nitrogenase FeMo cofactor and by a factor of 500 for Fermi-Hubbard model of 100 sites.

quant-ph↗

arXiv2025

Comprehensive Study on Heisenberg-limited Quantum Algorithms for Multiple Observables Estimation

In the accompanying paper of arXiv:2505.00697, we have presented a generalized scheme of adaptive quantum gradient estimation (QGE) algorithm, and further proposed two practical variants which not only achieve doubly quantum enhancement in query complexity regarding estimation precision and number of observables, but also enable minimal cost to estimate $k$-RDMs in fermionic systems among existing quantum algorithms. Here, we provide full descriptions on the algorithm, and provide theoretical guarantee for the estimation precision in terms of the root mean squared error. Furthermore, we analyze the performance of the quantum amplitude estimation algorithm, another variant of the Heisenberg-limited scaling algorithm, and show how the estimation error is minimized under the circuit structure that resembles the phase estimation algorithm. We finally describe the details for the numerical evaluation of the query complexity of the Heisenberg-limited algorithms and sampling-based methods to make a thorough comparison in the task of estimating fermionic $k$-RDMs.

quant-ph↗

arXiv2024

Hunting for quantum-classical crossover in condensed matter problems

The intensive pursuit for quantum advantage in terms of computational complexity has further led to a modernized crucial question: {\it When and how will quantum computers outperform classical computers?} The next milestone is undoubtedly the realization of quantum acceleration in practical problems. Here we provide a clear evidence and arguments that the primary target is likely to be condensed matter physics. Our primary contributions are summarized as follows: 1) Proposal of systematic error/runtime analysis on state-of-the-art classical algorithm based on tensor networks; 2) Dedicated and high-resolution analysis on quantum resource performed at the level of executable logical instructions; 3) Clarification of quantum-classical crosspoint for ground-state simulation to be within runtime of hours using only a few hundreds of thousand physical qubits for 2d Heisenberg and 2d Fermi-Hubbard models, assuming that logical qubits are encoded via the surface code with the physical error rate of $p=10^{-3}$. To our knowledge, we argue that condensed matter problems offer the earliest platform for demonstration of practical quantum advantage that is order-of-magnitude more feasible than ever known candidates, in terms of both qubit counts and total runtime.

quant-ph↗